[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F7NDYXMKD175VSPMPYR75W":3,"skill-same:01M6F7NDYXMKD175VSPMPYR75W":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6F7NDYXMKD175VSPMPYR75W","langchain-prod-checklist",null,"Execute LangChain production deployment checklist.\nUse when preparing for production launch, validating deployment readiness,\nor auditing existing production LangChain applications.\nTrigger with phrases like \"langchain production\", \"langchain prod ready\",\n\"deploy langchain\", \"langchain launch checklist\", \"production checklist\".","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1992,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-prod-checklist\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"langchain prod checklist","langchain","checklist","use","when","Existing","for","with","readiness","trigger","io","deployment checklist","ready","python","execute","edit","write","read","Bash","applications","Production","deployment","launch","2026-11-10 10:12:30",false,"---\nname: langchain-prod-checklist\ndescription: |\n  Execute LangChain production deployment checklist.\n  Use when preparing for production launch, validating deployment readiness,\n  or auditing existing production LangChain applications.\n  Trigger with phrases like \"langchain production\", \"langchain prod ready\",\n  \"deploy langchain\", \"langchain launch checklist\", \"production checklist\".\nallowed-tools: Read, Write, Edit, Bash(python:*)\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\n---\n\n# LangChain Production Checklist\n\n## Overview\nComprehensive checklist for deploying LangChain applications to production with reliability, security, and performance.\n\n## Prerequisites\n- LangChain application developed and tested\n- Infrastructure provisioned\n- CI\u002FCD pipeline configured\n\n## Production Checklist\n\n### 1. Configuration & Secrets\n- [ ] All API keys in secrets manager (not env vars in code)\n- [ ] Environment-specific configurations separated\n- [ ] Fallback values for non-critical settings\n- [ ] Configuration validation on startup\n\n```python\nfrom pydantic_settings import BaseSettings\nfrom pydantic import Field, SecretStr\n\nclass Settings(BaseSettings):\n    \"\"\"Validated configuration.\"\"\"\n    openai_api_key: SecretStr = Field(..., env=\"OPENAI_API_KEY\")\n    model_name: str = \"gpt-4o-mini\"\n    max_retries: int = Field(default=3, ge=1, le=10)\n    timeout_seconds: int = Field(default=30, ge=5, le=120)\n\n    class Config:\n        env_file = \".env\"\n\nsettings = Settings()  # Validates on import\n```\n\n### 2. Error Handling & Resilience\n- [ ] Retry logic with exponential backoff\n- [ ] Fallback models configured\n- [ ] Circuit breaker for cascading failures\n- [ ] Graceful degradation strategy\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_anthropic import ChatAnthropic\n\nprimary = ChatOpenAI(model=\"gpt-4o-mini\", max_retries=3)\nfallback = ChatAnthropic(model=\"claude-3-5-sonnet-20241022\")\n\nrobust_llm = primary.with_fallbacks([fallback])\n```\n\n### 3. Observability\n- [ ] Structured logging configured\n- [ ] Metrics collection enabled\n- [ ] Distributed tracing (LangSmith or OpenTelemetry)\n- [ ] Alerting rules defined\n\n```python\nimport os\n\n# LangSmith tracing\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = settings.langsmith_api_key\nos.environ[\"LANGCHAIN_PROJECT\"] = \"production\"\n\n# Prometheus metrics\nfrom prometheus_client import Counter, Histogram\n\nllm_requests = Counter(\"langchain_llm_requests_total\", \"Total LLM requests\")\nllm_latency = Histogram(\"langchain_llm_latency_seconds\", \"LLM latency\")\n```\n\n### 4. Performance\n- [ ] Caching configured for repeated queries\n- [ ] Connection pooling enabled\n- [ ] Timeout limits set\n- [ ] Batch processing for bulk operations\n\n```python\nfrom langchain_core.globals import set_llm_cache\nfrom langchain_community.cache import RedisCache\nimport redis\n\n# Production caching with Redis\nredis_client = redis.Redis.from_url(os.environ[\"REDIS_URL\"])\nset_llm_cache(RedisCache(redis_client))\n```\n\n### 5. Security\n- [ ] Input validation implemented\n- [ ] Output sanitization enabled\n- [ ] Rate limiting per user\u002FIP\n- [ ] Audit logging for all LLM calls\n\n```python\nfrom langchain_core.runnables import RunnableLambda\n\ndef validate_input(input_data: dict) -> dict:\n    \"\"\"Validate and sanitize input.\"\"\"\n    user_input = input_data.get(\"input\", \"\")\n    if len(user_input) > 10000:\n        raise ValueError(\"Input too long\")\n    return input_data\n\nsecure_chain = RunnableLambda(validate_input) | prompt | llm\n```\n\n### 6. Testing\n- [ ] Unit tests for all chains\n- [ ] Integration tests with mock LLMs\n- [ ] Load tests completed\n- [ ] Chaos engineering (failure injection)\n\n```python\n# pytest.ini\n[pytest]\nmarkers =\n    unit: Unit tests\n    integration: Integration tests\n    load: Load tests\n```\n\n### 7. Deployment\n- [ ] Health check endpoint\n- [ ] Graceful shutdown handling\n- [ ] Rolling deployment strategy\n- [ ] Rollback procedure documented\n\n```python\nfrom fastapi import FastAPI\nfrom contextlib import asynccontextmanager\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):\n    # Startup\n    print(\"Warming up LLM connections...\")\n    yield\n    # Shutdown\n    print(\"Cleaning up...\")\n\napp = FastAPI(lifespan=lifespan)\n\n@app.get(\"\u002Fhealth\")\nasync def health_check():\n    return {\"status\": \"healthy\", \"model\": settings.model_name}\n```\n\n### 8. Cost Management\n- [ ] Token counting implemented\n- [ ] Usage alerts configured\n- [ ] Cost allocation by tenant\u002Ffeature\n- [ ] Budget limits enforced\n\n```python\nimport tiktoken\n\ndef estimate_cost(text: str, model: str = \"gpt-4o-mini\") -> float:\n    \"\"\"Estimate API cost for text.\"\"\"\n    encoding = tiktoken.encoding_for_model(model)\n    tokens = len(encoding.encode(text))\n    # Approximate pricing (check current rates)\n    cost_per_1k = {\"gpt-4o-mini\": 0.00015, \"gpt-4o\": 0.005}\n    return (tokens \u002F 1000) * cost_per_1k.get(model, 0.001)\n```\n\n## Deployment Validation Script\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Pre-deployment validation script.\"\"\"\n\ndef run_checks():\n    checks = []\n\n    # Check 1: API key configured\n    try:\n        settings = Settings()\n        checks.append((\"API Key\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"API Key\", f\"FAIL: {e}\"))\n\n    # Check 2: LLM connectivity\n    try:\n        llm = ChatOpenAI(model=\"gpt-4o-mini\")\n        llm.invoke(\"test\")\n        checks.append((\"LLM Connection\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"LLM Connection\", f\"FAIL: {e}\"))\n\n    # Check 3: Cache connectivity\n    try:\n        redis_client.ping()\n        checks.append((\"Cache (Redis)\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"Cache (Redis)\", f\"FAIL: {e}\"))\n\n    for name, status in checks:\n        print(f\"[{status}] {name}\")\n\n    return all(\"PASS\" in status for _, status in checks)\n\nif __name__ == \"__main__\":\n    exit(0 if run_checks() else 1)\n```\n\n## Resources\n- [LangChain Production Guide](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fguides\u002Fproductionization\u002F)\n- [LangSmith](https:\u002F\u002Fdocs.smith.langchain.com\u002F)\n- [Twelve-Factor App](https:\u002F\u002F12factor.net\u002F)\n\n## Next Steps\nAfter launch, use `langchain-observability` for monitoring.\n","# LangChain Production Checklist\n\n## Overview\nComprehensive checklist for deploying LangChain applications to production with reliability, security, and performance.\n\n## Prerequisites\n- LangChain application developed and tested\n- Infrastructure provisioned\n- CI\u002FCD pipeline configured\n\n## Production Checklist\n\n### 1. Configuration & Secrets\n- [ ] All API keys in secrets manager (not env vars in code)\n- [ ] Environment-specific configurations separated\n- [ ] Fallback values for non-critical settings\n- [ ] Configuration validation on startup\n\n```python\nfrom pydantic_settings import BaseSettings\nfrom pydantic import Field, SecretStr\n\nclass Settings(BaseSettings):\n    \"\"\"Validated configuration.\"\"\"\n    openai_api_key: SecretStr = Field(..., env=\"OPENAI_API_KEY\")\n    model_name: str = \"gpt-4o-mini\"\n    max_retries: int = Field(default=3, ge=1, le=10)\n    timeout_seconds: int = Field(default=30, ge=5, le=120)\n\n    class Config:\n        env_file = \".env\"\n\nsettings = Settings()  # Validates on import\n```\n\n### 2. Error Handling & Resilience\n- [ ] Retry logic with exponential backoff\n- [ ] Fallback models configured\n- [ ] Circuit breaker for cascading failures\n- [ ] Graceful degradation strategy\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_anthropic import ChatAnthropic\n\nprimary = ChatOpenAI(model=\"gpt-4o-mini\", max_retries=3)\nfallback = ChatAnthropic(model=\"claude-3-5-sonnet-20241022\")\n\nrobust_llm = primary.with_fallbacks([fallback])\n```\n\n### 3. Observability\n- [ ] Structured logging configured\n- [ ] Metrics collection enabled\n- [ ] Distributed tracing (LangSmith or OpenTelemetry)\n- [ ] Alerting rules defined\n\n```python\nimport os\n\n# LangSmith tracing\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = settings.langsmith_api_key\nos.environ[\"LANGCHAIN_PROJECT\"] = \"production\"\n\n# Prometheus metrics\nfrom prometheus_client import Counter, Histogram\n\nllm_requests = Counter(\"langchain_llm_requests_total\", \"Total LLM requests\")\nllm_latency = Histogram(\"langchain_llm_latency_seconds\", \"LLM latency\")\n```\n\n### 4. Performance\n- [ ] Caching configured for repeated queries\n- [ ] Connection pooling enabled\n- [ ] Timeout limits set\n- [ ] Batch processing for bulk operations\n\n```python\nfrom langchain_core.globals import set_llm_cache\nfrom langchain_community.cache import RedisCache\nimport redis\n\n# Production caching with Redis\nredis_client = redis.Redis.from_url(os.environ[\"REDIS_URL\"])\nset_llm_cache(RedisCache(redis_client))\n```\n\n### 5. Security\n- [ ] Input validation implemented\n- [ ] Output sanitization enabled\n- [ ] Rate limiting per user\u002FIP\n- [ ] Audit logging for all LLM calls\n\n```python\nfrom langchain_core.runnables import RunnableLambda\n\ndef validate_input(input_data: dict) -> dict:\n    \"\"\"Validate and sanitize input.\"\"\"\n    user_input = input_data.get(\"input\", \"\")\n    if len(user_input) > 10000:\n        raise ValueError(\"Input too long\")\n    return input_data\n\nsecure_chain = RunnableLambda(validate_input) | prompt | llm\n```\n\n### 6. Testing\n- [ ] Unit tests for all chains\n- [ ] Integration tests with mock LLMs\n- [ ] Load tests completed\n- [ ] Chaos engineering (failure injection)\n\n```python\n# pytest.ini\n[pytest]\nmarkers =\n    unit: Unit tests\n    integration: Integration tests\n    load: Load tests\n```\n\n### 7. Deployment\n- [ ] Health check endpoint\n- [ ] Graceful shutdown handling\n- [ ] Rolling deployment strategy\n- [ ] Rollback procedure documented\n\n```python\nfrom fastapi import FastAPI\nfrom contextlib import asynccontextmanager\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):\n    # Startup\n    print(\"Warming up LLM connections...\")\n    yield\n    # Shutdown\n    print(\"Cleaning up...\")\n\napp = FastAPI(lifespan=lifespan)\n\n@app.get(\"\u002Fhealth\")\nasync def health_check():\n    return {\"status\": \"healthy\", \"model\": settings.model_name}\n```\n\n### 8. Cost Management\n- [ ] Token counting implemented\n- [ ] Usage alerts configured\n- [ ] Cost allocation by tenant\u002Ffeature\n- [ ] Budget limits enforced\n\n```python\nimport tiktoken\n\ndef estimate_cost(text: str, model: str = \"gpt-4o-mini\") -> float:\n    \"\"\"Estimate API cost for text.\"\"\"\n    encoding = tiktoken.encoding_for_model(model)\n    tokens = len(encoding.encode(text))\n    # Approximate pricing (check current rates)\n    cost_per_1k = {\"gpt-4o-mini\": 0.00015, \"gpt-4o\": 0.005}\n    return (tokens \u002F 1000) * cost_per_1k.get(model, 0.001)\n```\n\n## Deployment Validation Script\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Pre-deployment validation script.\"\"\"\n\ndef run_checks():\n    checks = []\n\n    # Check 1: API key configured\n    try:\n        settings = Settings()\n        checks.append((\"API Key\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"API Key\", f\"FAIL: {e}\"))\n\n    # Check 2: LLM connectivity\n    try:\n        llm = ChatOpenAI(model=\"gpt-4o-mini\")\n        llm.invoke(\"test\")\n        checks.append((\"LLM Connection\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"LLM Connection\", f\"FAIL: {e}\"))\n\n    # Check 3: Cache connectivity\n    try:\n        redis_client.ping()\n        checks.append((\"Cache (Redis)\", \"PASS\"))\n    except Exception as e:\n        checks.append((\"Cache (Redis)\", f\"FAIL: {e}\"))\n\n    for name, status in checks:\n        print(f\"[{status}] {name}\")\n\n    return all(\"PASS\" in status for _, status in checks)\n\nif __name__ == \"__main__\":\n    exit(0 if run_checks() else 1)\n```\n\n## Resources\n- [LangChain Production Guide](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fguides\u002Fproductionization\u002F)\n- [LangSmith](https:\u002F\u002Fdocs.smith.langchain.com\u002F)\n- [Twelve-Factor App](https:\u002F\u002F12factor.net\u002F)\n\n## Next Steps\nAfter launch, use `langchain-observability` for monitoring.\n",{"name":5,"author":47,"license":15,"version":48,"description":49,"allowed-tools":50},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Execute LangChain production deployment checklist.\nUse when preparing for production launch, validating deployment readiness,\nor auditing existing production LangChain applications.\nTrigger with phrases like \"langchain production\", \"langchain prod ready\",\n\"deploy langchain\", \"langchain launch checklist\", \"production checklist\".\n","Read, Write, Edit, Bash(python:*)","main","90b50ed0778745a80fd5bfb13b1843d45b9e82f7","tests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-prod-checklist",233,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-15 01:54:15",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill langchain-prod-checklist",{"tool":66,"command":67},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill langchain-prod-checklist",{"tool":69,"command":70},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill langchain-prod-checklist","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-prod-checklist\u002FSKILL.md",true,[],[75,114,137,163,189,211],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":80,"sourceHtmlUrl":81,"categories":82,"keywords":88,"publishedAt":42},"01M6FEGPPK0ZTKCCM0R3E3KS33","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[83],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,22,107,108,109,110,111,25,112,113],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","component","routing","state management","state","shadcn","file","ui","Tools","Tailwind","html","single-file","simple","ui components","multi","jsx","suite",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":118,"sourceHtmlUrl":119,"categories":120,"keywords":125,"publishedAt":42},"01M6FEE9VTK1W4GY21AKREJXRG","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},[126,127,128,94,129,130,90,131,132,133,104,134,38,135,136,26,25],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","behavior","local",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":42},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[145,146,147,148,149,150,151,152,22,153,154,155,156,157,158,159,26,160,161,162,25,23],"analysis","neuropixels analysis","spike","metrics","data","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":42},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000023","Skill与智能体开发",[174,175,176,177,178,179,180,181,182,183,184,185,186,187,26,25,24,23,188],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","new",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":193,"sourceHtmlUrl":194,"categories":195,"keywords":197,"publishedAt":42},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[196],{"categoryNo":171,"categoryName":172,"taxonomyType":86,"taxonomyTypeDesc":87},[191,198,199,200,201,202,203,133,204,205,22,206,207,208,209,25,210],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":79,"licenseFileUrl":215,"sourceHtmlUrl":216,"categories":217,"keywords":224,"publishedAt":42},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":222,"categoryName":223,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000016","图像与多媒体",[225,226,227,175,228,105,229,230,180,22,231,187,232,233,234,25,235,23],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]